Abstract
El-Nino is a meteorological/oceanographic phenomenon that occurs at irregular intervals of time (every few years) at low latitudes. El-Nino can be related to an annual weak warm ocean current that runs southward along the coast of Peru and Ecuador about Christmastime. It is characterized by unusually large warming that occurs every few years and changes the local and regional ecology. El-Nino has been linked to climate change anomalies like global warming, etc. The data for this work has been taken from the websites mainly for India (Becker in Impacts of El-Nino and La Niña on the hurricane season, 2014 [1]; Hansen et al. in GISS surface temperature analysis (GISTEMP) NASA goddard institute for space studies, 2017 [2]; Cook in Pacific marine environmental laboratory national oceanic and atmospheric administration, 1999 [3]; Climate Prediction Center—Monitoring & Data [4]; Romm in Climate Deniers’ favorite temperature dataset just confirmed global warming, 2016 [5]; World Bank Group, 2017 [6]; National Center for Atmospheric Research Staff (Eds) in The climate data guide: global temperature data sets: overview & comparison table, 2014 [7]; Global Climate Change Data, 1750–2015 [8]). Data have been preprocessed using imputation, F-measure, and maximum likelihood missing value methods. Finally, the prediction has been made about the time of occurrence of the next El-Nino year by using a multiple linear regression algorithm. A comparative analysis has been done on the three approaches used. The work also calculates Karl Pearson’s correlation coefficient between global warming and temperature change, temperature change and El-Nino, and finally global warming and El-Nino. Performance analysis has been done on the correlation coefficient calculated.
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Nitima, M., Jyoti, G., Nisha, B. (2019). Prediction of El-Nino Year and Performance Analysis on the Calculated Correlation Coefficients. In: Kapur, P., Klochkov, Y., Verma, A., Singh, G. (eds) System Performance and Management Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7323-6_15
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